Comparison of Damage Detection Methods Depending on FRFs within Specified Frequency Ranges

Yong-Su Kim,Hee-Chang Eun

doi:10.1155/2017/5821835

Abstract

Structural damage can be detected using frequency response function (FRF) measured by an impact and the corresponding responses. The change in the mechanical properties of dynamic system for damage detection can seldom be estimated using FRF data extracted from a very limited frequency range. Proper orthogonal modes (POMs) from the FRFs extracted in given frequency ranges and their modified forms can be utilized as damage indices to detect damage. The POM-based damage detection methods must be sensitive to the selected FRFs. This work compares the effectiveness of the damage detection approaches taking the POMs estimated by the FRFs within five different frequency ranges including resonance frequency and antiresonance frequency. It is shown from a numerical example that the POMs extracted from the FRFs within antiresonance frequency ranges provide more explicit information on the damage locations than the ones within resonance frequency ranges.

Highlights

Structural damage is detected based on the variation in dynamic responses due to the local change of physical properties at damage region
Beginning with the frequency response function (FRF) measured from the dynamic finite element model, this work investigates the sensitivity of the damage detection method using the Proper orthogonal modes (POMs) to be estimated from the FRFs corresponding to five different frequency ranges including resonance frequency and antiresonance frequency
The damage detection method can be explicitly and widely carried out by the three damage indices of the POM corresponding to the first POV to extract the FRFs in the first and third antiresonance frequencies despite the external noise

Summary

Introduction

Structural damage is detected based on the variation in dynamic responses due to the local change of physical properties at damage region. Rahmatalla et al [6] presented a feasible method for structural vibration-based health monitoring to reduce the dimension of the initial FRF data and to employ artificial neural network. Feeny and Kappagantu [17] presented a damage detection method based on fuzzy c-means clustering algorithm and measured FRF data reduced by principal component analysis. The POM can be changed depending on the FRF data sets extracted from full sets of FRFs. Beginning with the FRFs measured from the dynamic finite element model, this work investigates the sensitivity of the damage detection method using the POMs to be estimated from the FRFs corresponding to five different frequency ranges including resonance frequency and antiresonance frequency. A numerical example compares the sensitivity of the damage detection method depending on the extracted FRFs

FRF and POM

Numerical Experiment

Findings

Conclusions